click here to download project base paper of nlp machine learning
click here to download project abstract
At DataPro, we provide final year projects with source code in python for computer science students in Hyderabad , Visakhapatnam.
ABSTRACT
Introduction: Depression is a pervasive mental health issue, and early detection is crucial for effective intervention. Traditional methods often rely on self-reporting, which may be biased. Therefore, this study proposes a technology-driven solution for more objective and timely identification of depressive symptoms.
OCR Integration: The first component of our approach involves the use of OCR technology to extract textual information from various sources, such as social media posts, online forums, and diaries. so This allows for the automated collection of textual data related to individuals’ thoughts, feelings, and experiences, creating a comprehensive dataset for analysis.
NLP Analysis: The extracted text undergoes thorough NLP analysis, employing advanced linguistic algorithms to discern patterns, sentiments, and linguistic markers associated with depression. Our model focuses on identifying key linguistic features indicative of depressive states, such as changes in tone, expressions of hopelessness, and frequency of negative keywords. This phase facilitates the transformation of raw textual data into meaningful insights about individuals’ mental well-being.
Machine Learning Integration: These datasets consist of both positive and negative instances of depression, allowing the model to learn and generalize patterns indicative of depressive symptoms. The integration of machine learning ensures the adaptability and scalability of the system, enabling it to continuously improve its predictive capabilities over time.
Conclusion: By combining OCR and NLP technologies, this research presents a novel and effective approach to depression detection. so This technology-driven solution offers a promising avenue for early intervention and support for individuals experiencing depression.
